Analysis of MODIS imagery for detection of clear cuts in the boreal forest in north-west Russia

Bucha, Tomáš; Stibig, Hans-Jürgen

doi:10.1016/j.rse.2007.11.008

Cited by 33 publications

(26 citation statements)

References 24 publications

(39 reference statements)

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“…This approach was developed earlier to assess the accuracy of clear-cut detection derived from MODIS imagery in boreal forest [17]. The three thresholds identified by the model were used to create three binary layers from the breakpoint magnitude layer classified as "clear-cut" and "no clear-cut".…”

Section: Assessment Of the Accuracy Of The Clear-cut Detection Modelmentioning

confidence: 99%

“…The accuracy of the results usually depends on the magnitude of the signal, the size of the area undergoing change and the degree of forest stand fragmentation [16]. Several authors have suggested methods for detecting clear-cuts or deforestation using coarse spatial resolution (e.g., 250-1000 m) satellite data [17,18]. These detection methods are based on changes in spectra or in texture between two dates.…”

Section: Introductionmentioning

confidence: 99%

“…These authors highlighted that methods based on changes in texture were the least accurate. Clear-cuts that extend over less than 15 ha are difficult to detect due to the high number of errors of commission [17]. Morton et al reported that the most appropriate method for detecting clear-cuts using MODIS images depends on what the user needs [19].…”

Section: Introductionmentioning

confidence: 99%

“…These may go from the need to detect all impacted areas with significant errors of commission to a need for a more accurate detection of clear-cut areas with significant errors of omission. Detection methods can be based on the binary "change-no change" classification [17,18] or probabilistic approaches [16,20]. Probabilistic approaches have some advantages over binary change methods because users can select the appropriate threshold according to their needs [21].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Clear-Cuts and Decreases in Forest Vitality Using MODIS NDVI Time Series

et al. 2015

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This paper examines the potential of MODIS-NDVI time series for detecting clear-cuts in a coniferous forest stand in the south of France. The proposed approach forms part of a survey monitoring the status of forest health and evaluating the forest decline phenomena observed over the last few decades. One of the prerequisites for this survey was that a rapid and easily reproducible method had to be developed that differentiates between forest clear-cuts and changes in forest health induced by environmental factors such as summer droughts. The proposed approach is based on analysis of the breakpoints detected within NDVI time series, using the "Break for Additive Seasonal and Trend" (BFAST) algorithm. To overcome difficulties detecting small areas on the study site, we chose a probabilistic approach based on the use of a conditional inference tree. For model calibration, clear-cut reference data were produced at MODIS resolution (250 m). According to the magnitude of the detected breakpoints, probability classes for the presence of clear-cuts were defined, from greater than 90% to less than 3% probability of a clear-cut. One of the advantages of the probabilistic model is that it allows end users to choose an acceptable level of uncertainty depending on the application. In addition, the use of BFAST allows events to be dated, thus OPEN ACCESSRemote Sens. 2015, 7 3589 making it possible to perform a retrospective analysis of decreases in forest vitality in the study area.

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Section: Assessment Of the Accuracy Of The Clear-cut Detection Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting Clear-Cuts and Decreases in Forest Vitality Using MODIS NDVI Time Series

et al. 2015

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“…Although suitable for detection of large--area disturbances such as fire and large--scale clearcuts, coarse--resolution imagery is less capable of detecting forest management activities than finer--grained observations from satellites such as Landsat (Skole and Tucker, 1993; Tucker and 5 Townshend, 2000;Bucha & Stibig, 2008;Wulder et al, 2008;Potapov et al, 2009). Studies that use imagery of finer spatial grain have either mapped at too coarse a temporal grain to detect short--term changes in forest disturbance rate Hansen et al, 2010) The study reported here was derived from the first two phases of NAFD, which employed a geographic sample of Landsat observations at a relatively high temporal frequency (approximately annual time step) over a 20--year period to characterize the dynamics of recent US forest disturbance history.…”

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confidence: 99%

United States Forest Disturbance Trends Observed Using Landsat Time Series

et al. 2013

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Disturbance events strongly affect the composition, structure, and function of forest ecosystems; however, existing U.S. land management inventories were not designed to monitor disturbance. To begin addressing this gap, the North American Forest Dynamics (NAFD) project has examined a geographic sample of 50 Landsat satellite image time series to assess trends in forest disturbance across the conterminous United States for 1985--2005. The geographic sample design used a probability--based scheme to encompass major forest types and maximize geographic dispersion. For each sample location disturbance was identified in the Landsat series using the Vegetation Change Tracker (VCT) algorithm.The NAFD analysis indicates that, on average, 2.77 Mha/yr of forests were disturbed annually, representing 1.09%/yr of US forestland. These satellite--based national disturbance rates estimates tend to be lower than those derived from land management inventories, reflecting both methodological and definitional differences. In particular the VCT approach used with a biennial time step has limited sensitivity to low--intensity disturbances. Unlike prior satellite studies, our biennial forest disturbance rates vary by nearly a factor of two between high and low years. High western US disturbance rates were associated with active fire years and insect activity, while variability in the east is more strongly related to harvest rates in managed forests. We note that generating a geographic sample based on representing forest type and variability may be problematic since the spatial pattern of disturbance does not necessarily correlate with forest type. We also find that the prevalence of diffuse, non--stand clearing disturbance in US forests makes the application of a biennial geographic sample problematic. Future satellite--based studies of disturbance at regional and national scales should focus on wall--to--wall analyses with annual time step for improved accuracy. IntroductionChange is ubiquitous in forest ecosystems. Forests experience both seasonality as well as long--term growth cycles that can vary in duration between 50 years and 500 or more years (Waring & Running, 2007). These long--term changes are punctuated by mostly short--term disturbances from fire, insects, disease, and harvest which strongly alter the state and functioning of the forest (He & Mlandenoff, 1999). Both climate change and the increasing global demand for wood and fiber products are likely to drive increases in forest disturbance rates Nepstad et al., 2008). These changes in disturbance will alter the water and carbon cycles of forest stands as well as impact the habitat and biodiversity of these ecosystems (Lindenmayer et al., 2006;Gardner et al., 2009). With respect to the carbon cycle, forest disturbance is now recognized as a major driver of non--fossil--fuels--related terrestrial fluxes to the atmosphere (Running, 2008; Amiro et al., 2010).To effectively understand how forest disturbance impacts forest state and functioning, disturbance rates...

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Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests

Torresan

Luyssaert

Filippa

et al. 2021

Climate-Smart Forestry in Mountain Regions

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Monitoring forest responses to climate-smart forestry (CSF) is necessary to determine whether forest management is on track to contribute to the reduction and/or removal of greenhouse gas emissions and the development of resilient mountain forests. A set of indicators to assess “the smartness” of forests has been previously identified by combining indicators for sustainable forest management with the ecosystem services. Here, we discuss the remote sensing technologies suitable to assess those indicators grouped in forest resources, health and vitality, productivity, biological diversity, and protective functions criteria. Forest cover, growing stock, abiotic, biotic, and human-induced forest damage, and tree composition indicators can be readily assessed by using established remote sensing techniques. The emerging areas of phenotyping will help track genetic resource indicators. No single existing sensor or platform is sufficient on its own to assess all the individual CSF indicators, due to the need to balance fine-scale monitoring and satisfactory coverage at broad scales. The challenge of being successful in assessing the largest number and type of indicators (e.g., soil conditions) is likely to be best tackled through multimode and multifunctional sensors, increasingly coupled with new computational and analytical approaches, such as cloud computing, machine learning, and deep learning.

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Analysis of MODIS imagery for detection of clear cuts in the boreal forest in north-west Russia

Cited by 33 publications

References 24 publications

Detecting Clear-Cuts and Decreases in Forest Vitality Using MODIS NDVI Time Series

Detecting Clear-Cuts and Decreases in Forest Vitality Using MODIS NDVI Time Series

United States Forest Disturbance Trends Observed Using Landsat Time Series

Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests

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